发表机构
Southeast University; Hainan University（东南大学; 海南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究一体化图像恢复问题，提出QuReC框架，含退化引导的查询重建模块和局部-全局响应校准模块，通过特定匹配和双分支聚合校准实现细粒度恢复引导，在多个基准上性能优异。
AI 中文摘要
一体化图像恢复旨在用单一统一模型恢复因多种损坏类型而退化的干净图像。现有方法通常依赖图像级提示或共享引导来处理各种退化。然而，当退化在空间上异质或在单个图像中以混合形式共存时，这种范式就不够了。仅空间自适应引导也不够，因为准确恢复还需要每个空间查询可靠地聚合来自局部邻域和全局上下文的互补信息。为此，我们提出了QuReC，一个一体化图像恢复的统一框架。QuReC由退化引导的查询重建模块（DQRM）和局部-全局响应校准模块（LGRCM）组成。具体来说，DQRM将每个空间查询与退化原型空间进行匹配，以重建特定于查询的退化感知表示，从而提供细粒度的空间自适应恢复引导。为了进一步稳定这种逐查询匹配过程，我们引入了弱监督原型匹配学习策略，以提高优化稳定性和退化语义一致性。同时，LGRCM执行局部-全局双分支聚合，并用可学习的先验校准聚合响应，提高特征聚合的可靠性以及局部细节建模和全局上下文建模之间的协调性。大量实验表明，QuReC在多个一体化图像恢复基准上取得了优异的性能。代码已在该https URL发布。
英文摘要
All-in-one image restoration aims to recover clean images degraded by multiple corruption types using a single unified model. Existing methods typically rely on image-level prompts or shared guidance to handle diverse degradations. However, such a paradigm becomes inadequate when degradations are spatially heterogeneous or even coexist in mixed forms within a single image. Yet spatially adaptive guidance alone is not sufficient, since accurate restoration also requires each spatial query to reliably aggregate complementary information from local neighborhoods and global contexts. To this end, we propose QuReC, a unified framework for all-in-one image restoration. QuReC consists of a Degradation-Guided Query Reconstruction Module (DQRM) and a Local-Global Response Calibration Module (LGRCM). Specifically, DQRM matches each spatial query against a degradation prototype space to reconstruct a query-specific degradation-aware representation, thereby providing fine-grained spatially adaptive restoration guidance. To further stabilize this query-wise matching process, we introduce a weakly supervised prototype matching learning strategy to improve optimization stability and degradation semantic consistency. Meanwhile, LGRCM performs local-global dual-branch aggregation and calibrates the aggregated responses with learnable priors, improving the reliability of feature aggregation and the coordination between local detail modeling and global context modeling. Extensive experiments demonstrate that QuReC achieves superior performance on multiple all-in-one image restoration benchmarks. The code is released at https://github.com/zhoushen1/QuReC.
CommentsAccepted by ACM MM 2026
